{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import cv2\n",
    "import os\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "image_dir = '/home/gaurav/datasets/agri/Tomato_Classifier/data/'\n",
    "\n",
    "healthy_images_dir = image_dir + 'Healthy/'\n",
    "unhealthy_images_dir = image_dir + 'UnHealthy/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "healthy_image_files = [(healthy_images_dir + '/'+ f)  \n",
    "                       for f in os.listdir(healthy_images_dir) \n",
    "                       if f.endswith('.jpg')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['/home/gaurav/datasets/agri/Tomato_Classifier/data/Healthy//1e1aa3d8-d12f-47e1-b316-b8656ab3f2b6___RS_HL 0075_final_masked.jpg',\n",
       " '/home/gaurav/datasets/agri/Tomato_Classifier/data/Healthy//3dd714a8-5c95-4d47-a9d0-0ac15a499c2c___RS_HL 0386_final_masked.jpg',\n",
       " '/home/gaurav/datasets/agri/Tomato_Classifier/data/Healthy//7b651761-da31-437a-be17-6e49912f622a___RS_HL 9703_final_masked.jpg',\n",
       " '/home/gaurav/datasets/agri/Tomato_Classifier/data/Healthy//2df73051-0fdf-4ed5-a626-205e245ad8c7___GH_HL Leaf 233_final_masked.jpg',\n",
       " '/home/gaurav/datasets/agri/Tomato_Classifier/data/Healthy//3e9a67e4-492b-4306-be1b-0a2624ea5c3e___GH_HL Leaf 175_final_masked.jpg']"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "healthy_image_files[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "unhealthy_image_files = [(unhealthy_images_dir + '/'+ f)  \n",
    "                         for f in os.listdir(unhealthy_images_dir) \n",
    "                         if f.endswith('.jpg')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['/home/gaurav/datasets/agri/Tomato_Classifier/data/UnHealthy//2ba2d560-919c-48cd-8f98-c4caf8fc37e7___Crnl_L.Mold 8994_final_masked.jpg',\n",
       " '/home/gaurav/datasets/agri/Tomato_Classifier/data/UnHealthy//0db67be3-f733-4d15-b7d9-5d075b2cafc7___RS_Erly.B 6401_final_masked.jpg',\n",
       " '/home/gaurav/datasets/agri/Tomato_Classifier/data/UnHealthy//4cec5b1e-14d2-4b8f-ab8a-57b68758f30c___Crnl_L.Mold 8715_final_masked.jpg',\n",
       " '/home/gaurav/datasets/agri/Tomato_Classifier/data/UnHealthy//0f1d337f-e8c5-445a-83a0-b89915eacb72___UF.GRC_BS_Lab Leaf 0435_final_masked.jpg',\n",
       " '/home/gaurav/datasets/agri/Tomato_Classifier/data/UnHealthy//0d835caf-0069-4fa5-a022-c8cc7763189c___Com.G_SpM_FL 8917_final_masked.jpg']"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unhealthy_image_files[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "healthy_images = [cv2.imread(img) for img in healthy_image_files]\n",
    "unhealthy_images = [cv2.imread(img) for img in unhealthy_image_files]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Pre-processing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Healthy Leaf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f8dc03c4e10>"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TKffu3QPoFt9sNptu/Nv3feqiJohDLl+5Qugn3Hr9DqU0PPniczQYRqMRX//j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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8dc04004e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow( healthy_images[4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "500"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(healthy_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(256, 256, 3)\n"
     ]
    }
   ],
   "source": [
    "image_shape = healthy_images[0].shape\n",
    "print( image_shape )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(healthy_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500, 256, 256, 3)"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert list to numpy array\n",
    "healthy_images_np = np.array(healthy_images)\n",
    "healthy_images_np.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Unhealthy Leaf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f8dc03b9978>"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mppmdnaVcKzI1Ocbe/i5Wt0O73UYEPpqiIISKmtCRNQUv8gmCAElWUHQVz/Po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0h3+M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\nxTaUMCgUim38f08ifiNM+lb3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8e420f05c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow( unhealthy_images[4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "540"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(unhealthy_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(540, 256, 256, 3)"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unhealthy_images_np = np.array( unhealthy_images )\n",
    "unhealthy_images_np.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating labels array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_labels = [0] * len( healthy_images ) + [1] * len( unhealthy_images )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1040"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(y_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_labels[495:510]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Convert labels to numpy array\n",
    "y_labels_np = np.array( y_labels )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combining healthy and unhealthy images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "all_images_np = np.concatenate( (healthy_images_np, unhealthy_images_np), axis = 0 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1040, 256, 256, 3)"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_images_np.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1040"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(all_images_np)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Splitting train and test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "all_indexes = list( range(len(all_images_np)) )\n",
    "test_indexes = random.sample( all_indexes, 300 )\n",
    "train_indexes = list( set( all_indexes ) - set( test_indexes ) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Train images\n",
    "X_train = all_images_np[train_indexes]\n",
    "y_train = y_labels_np[train_indexes]\n",
    "\n",
    "## Test images\n",
    "X_test = all_images_np[test_indexes]\n",
    "y_test = y_labels_np[test_indexes]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sanity Check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def sanity_checker( idx ):\n",
    "    plt.imshow( X_train[idx] );\n",
    "    plt.show();\n",
    "    print( y_train[idx])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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78w3WMDrYp9YRWd3yhS9+keeefx6hFMfTc4xt0cbi92LkNKQRsGxqhrtb9Md9bjz/HOl4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\nmGGGGW7BzDHMMMMMt2DmGGaYYYZb8P8BWE+fiU27UZAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8dc038ca20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    }
   ],
   "source": [
    "sanity_checker( 634 )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build the Model for Classifying Healthy or Unhealthy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import keras\n",
    "import tensorflow as tf\n",
    "from keras import backend as K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# import necessary building blocks\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Activation, Dropout\n",
    "from keras.layers.advanced_activations import LeakyReLU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# normalize inputs\n",
    "X_train_norm = (X_train/255) - 0.5\n",
    "X_test_norm = (X_test/255) - 0.5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model 1:\n",
    "\n",
    "** Architecture **\n",
    "- Conv2D -> MaxPool -> Conv2D -> MaxPool -> Dense -> Dense -> Sigmoid\n",
    "\n",
    "** Optimizer **\n",
    "\n",
    "- SGD\n",
    "- Batch size = 32\n",
    "- Epoch = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "K.clear_session()  # clear default graph\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Conv2D(filters=8, kernel_size=(3,3), strides=1, input_shape=image_shape))\n",
    "model.add(LeakyReLU(0.1))\n",
    "                            \n",
    "model.add(MaxPooling2D(pool_size=(3, 3)))\n",
    "\n",
    "model.add(Conv2D(filters=16, kernel_size=(3,3), strides=1, input_shape=image_shape))\n",
    "model.add(LeakyReLU(0.1))\n",
    "                            \n",
    "model.add(MaxPooling2D(pool_size=(3, 3)))\n",
    "\n",
    "model.add(Flatten())\n",
    "    \n",
    "model.add(Dense(16))\n",
    "model.add(LeakyReLU(0.1))\n",
    "\n",
    "model.add(Dense(1))\n",
    "model.add(Activation('sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 254, 254, 8)       224       \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_1 (LeakyReLU)    (None, 254, 254, 8)       0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 84, 84, 8)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 82, 82, 16)        1168      \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_2 (LeakyReLU)    (None, 82, 82, 16)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 27, 27, 16)        0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 11664)             0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 16)                186640    \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_3 (LeakyReLU)    (None, 16)                0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 17        \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 1)                 0         \n",
      "=================================================================\n",
      "Total params: 188,049\n",
      "Trainable params: 188,049\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.compile(optimizer='sgd',\n",
    "              loss = 'binary_crossentropy',\n",
    "              metrics = ['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "BATCH_SIZE = 32\n",
    "EPOCHS = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 740 samples, validate on 300 samples\n",
      "Epoch 1/20\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.6891 - acc: 0.6162 - val_loss: 0.6871 - val_acc: 0.6200\n",
      "Epoch 2/20\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.6841 - acc: 0.6568 - val_loss: 0.6814 - val_acc: 0.6900\n",
      "Epoch 3/20\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.6776 - acc: 0.7081 - val_loss: 0.6826 - val_acc: 0.4967\n",
      "Epoch 4/20\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.6720 - acc: 0.6122 - val_loss: 0.6665 - val_acc: 0.7467\n",
      "Epoch 5/20\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.6618 - acc: 0.6892 - val_loss: 0.6557 - val_acc: 0.7333\n",
      "Epoch 6/20\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.6496 - acc: 0.7257 - val_loss: 0.6406 - val_acc: 0.7300\n",
      "Epoch 7/20\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.6324 - acc: 0.7432 - val_loss: 0.6309 - val_acc: 0.6533\n",
      "Epoch 8/20\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.6142 - acc: 0.7270 - val_loss: 0.5973 - val_acc: 0.7600\n",
      "Epoch 9/20\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.5875 - acc: 0.7324 - val_loss: 0.6486 - val_acc: 0.5800\n",
      "Epoch 10/20\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.5664 - acc: 0.7351 - val_loss: 0.5808 - val_acc: 0.6700\n",
      "Epoch 11/20\n",
      "740/740 [==============================] - 11s 14ms/step - loss: 0.5544 - acc: 0.7351 - val_loss: 0.5526 - val_acc: 0.7000\n",
      "Epoch 12/20\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.5349 - acc: 0.7446 - val_loss: 0.5082 - val_acc: 0.7867\n",
      "Epoch 13/20\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.5181 - acc: 0.7500 - val_loss: 0.4988 - val_acc: 0.7667\n",
      "Epoch 14/20\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.5018 - acc: 0.7527 - val_loss: 0.5047 - val_acc: 0.7533\n",
      "Epoch 15/20\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.4855 - acc: 0.7743 - val_loss: 0.6994 - val_acc: 0.5967\n",
      "Epoch 16/20\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.5137 - acc: 0.7378 - val_loss: 0.7707 - val_acc: 0.5633\n",
      "Epoch 17/20\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.4966 - acc: 0.7743 - val_loss: 0.4585 - val_acc: 0.8100\n",
      "Epoch 18/20\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.4635 - acc: 0.7851 - val_loss: 0.4373 - val_acc: 0.8100\n",
      "Epoch 19/20\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.4525 - acc: 0.7919 - val_loss: 0.4496 - val_acc: 0.7733\n",
      "Epoch 20/20\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.4355 - acc: 0.8081 - val_loss: 0.4291 - val_acc: 0.7933\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(\n",
    "    X_train_norm, \n",
    "    y_train,  # prepared data\n",
    "    batch_size=BATCH_SIZE,\n",
    "    epochs=EPOCHS,\n",
    "    validation_data=(X_test_norm, y_test),\n",
    "    verbose=1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['val_loss', 'val_acc', 'loss', 'acc'])"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history.history.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### function: train and test accuracy plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_train_val_accuracy(hist):\n",
    "    plt.plot(hist['acc'])\n",
    "    plt.plot(hist['val_acc'])\n",
    "    plt.title('model accuracy')\n",
    "    plt.ylabel('accuracy')\n",
    "    plt.xlabel('epoch')\n",
    "    plt.legend(['train', 'test'], loc='upper left')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### function: train and test loss plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_train_val_loss(hist):\n",
    "    plt.plot(hist['loss'])\n",
    "    plt.plot(hist['val_loss'])\n",
    "    plt.title('model loss')\n",
    "    plt.ylabel('loss')\n",
    "    plt.xlabel('epoch')\n",
    "    plt.legend(['train', 'test'], loc='upper left')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4sgn6jFb5ClDOAuDEoa7lLDI3QG05i4qH8sxT31BQXkN0SADXnJbAZePiGd43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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8dd82a3b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_train_val_accuracy(history.history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/g7fDp/PNne05lZHFoE+X8Nr0TZzJLMFvhhumwMH10OP5wv1W6+VlJZU9S+Gg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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8dd82a3a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_train_val_loss(history.history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model 2: Dealing with Variations in GD Loss\n",
    "\n",
    "** Architecture **\n",
    "- Conv2D -> MaxPool -> Conv2D -> MaxPool -> Dense -> Dense -> Sigmoid\n",
    "\n",
    "** Optimizer **\n",
    "\n",
    "- Adam (change)\n",
    "- Batch size = 32\n",
    "- Epoch = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "K.clear_session()  # clear default graph\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Conv2D(filters=8, kernel_size=(3,3), strides=1,input_shape=image_shape))\n",
    "model.add(LeakyReLU(0.1))\n",
    "                            \n",
    "model.add(MaxPooling2D(pool_size=(3, 3)))\n",
    "\n",
    "model.add(Conv2D(filters=16, kernel_size=(3,3), strides=1, input_shape=image_shape))\n",
    "model.add(LeakyReLU(0.1))\n",
    "                            \n",
    "model.add(MaxPooling2D(pool_size=(3, 3)))\n",
    "\n",
    "model.add(Flatten())\n",
    "    \n",
    "model.add(Dense(16))\n",
    "model.add(LeakyReLU(0.1))\n",
    "\n",
    "model.add(Dense(1))\n",
    "model.add(Activation('sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 740 samples, validate on 300 samples\n",
      "Epoch 1/20\n",
      "740/740 [==============================] - 11s 14ms/step - loss: 0.6851 - acc: 0.6432 - val_loss: 0.6110 - val_acc: 0.6967\n",
      "Epoch 2/20\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.5513 - acc: 0.7473 - val_loss: 0.4815 - val_acc: 0.7867\n",
      "Epoch 3/20\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.4571 - acc: 0.7757 - val_loss: 0.4823 - val_acc: 0.7467\n",
      "Epoch 4/20\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.4047 - acc: 0.8230 - val_loss: 0.3909 - val_acc: 0.8200\n",
      "Epoch 5/20\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.3802 - acc: 0.8297 - val_loss: 0.3658 - val_acc: 0.8300\n",
      "Epoch 6/20\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.2991 - acc: 0.8824 - val_loss: 0.3153 - val_acc: 0.8633\n",
      "Epoch 7/20\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.2368 - acc: 0.9149 - val_loss: 0.2273 - val_acc: 0.9233\n",
      "Epoch 8/20\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.1723 - acc: 0.9459 - val_loss: 0.1873 - val_acc: 0.9233\n",
      "Epoch 9/20\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.1335 - acc: 0.9500 - val_loss: 0.1811 - val_acc: 0.9433\n",
      "Epoch 10/20\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.0990 - acc: 0.9716 - val_loss: 0.1943 - val_acc: 0.9200\n",
      "Epoch 11/20\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.1080 - acc: 0.9635 - val_loss: 0.1529 - val_acc: 0.9400\n",
      "Epoch 12/20\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.0746 - acc: 0.9784 - val_loss: 0.1948 - val_acc: 0.9233\n",
      "Epoch 13/20\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.0534 - acc: 0.9878 - val_loss: 0.1388 - val_acc: 0.9567\n",
      "Epoch 14/20\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.0375 - acc: 0.9892 - val_loss: 0.1888 - val_acc: 0.9333\n",
      "Epoch 15/20\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.0450 - acc: 0.9851 - val_loss: 0.1890 - val_acc: 0.9333\n",
      "Epoch 16/20\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.0368 - acc: 0.9946 - val_loss: 0.1393 - val_acc: 0.9600\n",
      "Epoch 17/20\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.0224 - acc: 0.9973 - val_loss: 0.2050 - val_acc: 0.9300\n",
      "Epoch 18/20\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.0171 - acc: 0.9986 - val_loss: 0.1332 - val_acc: 0.9533\n",
      "Epoch 19/20\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.0111 - acc: 0.9986 - val_loss: 0.1427 - val_acc: 0.9633\n",
      "Epoch 20/20\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.0088 - acc: 0.9986 - val_loss: 0.1496 - val_acc: 0.9533\n"
     ]
    }
   ],
   "source": [
    "model.compile(optimizer='adam',\n",
    "              loss = 'binary_crossentropy',\n",
    "              metrics = ['accuracy'])\n",
    "\n",
    "BATCH_SIZE = 32\n",
    "EPOCHS = 20\n",
    "\n",
    "history = model.fit(\n",
    "    X_train_norm, \n",
    "    y_train,  # prepared data\n",
    "    batch_size=BATCH_SIZE,\n",
    "    epochs=EPOCHS,\n",
    "    validation_data=(X_test_norm, y_test),\n",
    "    verbose=1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZcstJNZ8UsKFlbnTtGLu8act6jHIjj61h2RxGGsUa3MZUKXUZJM+CM+4nOUu5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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8de9fa7748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_train_val_accuracy(history.history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3jOO/n/9GWnZBPQdojDHus6RwgAicMB2SV8G2RZUUEe46uw85hSX85/Pf6jlAY4xxnyWF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\ncGD/lY9PgKc9+/dXIKGe/75hOAf56HK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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8e3380c6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_train_val_loss(history.history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model 3: Dealing with Overfitting\n",
    "\n",
    "** Architecture **\n",
    "- Conv2D -> MaxPool -> Conv2D -> MaxPool -> Dense -> Dropout -> Dense -> Dropout -> (Dense -> Sigmoid)\n",
    "\n",
    "** Optimizer **\n",
    "\n",
    "- Adam\n",
    "- Batch size = 32\n",
    "- Epoch = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "K.clear_session()  # clear default graph\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Conv2D(filters=8, kernel_size=(3,3), strides=1,input_shape=image_shape))\n",
    "model.add(LeakyReLU(0.1))\n",
    "                            \n",
    "model.add(MaxPooling2D(pool_size=(3, 3)))\n",
    "\n",
    "model.add(Conv2D(filters=16, kernel_size=(3,3), strides=1, input_shape=image_shape))\n",
    "model.add(LeakyReLU(0.1))\n",
    "                            \n",
    "model.add(MaxPooling2D(pool_size=(3, 3)))\n",
    "\n",
    "model.add(Flatten())\n",
    "    \n",
    "model.add(Dense(32))\n",
    "model.add(LeakyReLU(0.1))\n",
    "model.add(Dropout(0.5))\n",
    "\n",
    "model.add(Dense(8))\n",
    "model.add(LeakyReLU(0.1))\n",
    "model.add(Dropout(0.5))\n",
    "\n",
    "model.add(Dense(1))\n",
    "model.add(Activation('sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 740 samples, validate on 300 samples\n",
      "Epoch 1/20\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.7587 - acc: 0.4892 - val_loss: 0.6703 - val_acc: 0.6300\n",
      "Epoch 2/20\n",
      "740/740 [==============================] - 11s 14ms/step - loss: 0.6578 - acc: 0.6122 - val_loss: 0.6463 - val_acc: 0.7367\n",
      "Epoch 3/20\n",
      "740/740 [==============================] - 11s 14ms/step - loss: 0.6553 - acc: 0.6149 - val_loss: 0.5831 - val_acc: 0.7733\n",
      "Epoch 4/20\n",
      "740/740 [==============================] - 11s 14ms/step - loss: 0.5960 - acc: 0.6811 - val_loss: 0.5698 - val_acc: 0.7833\n",
      "Epoch 5/20\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.5172 - acc: 0.7541 - val_loss: 0.4805 - val_acc: 0.7800\n",
      "Epoch 6/20\n",
      "740/740 [==============================] - 11s 14ms/step - loss: 0.4598 - acc: 0.7892 - val_loss: 0.4018 - val_acc: 0.8833\n",
      "Epoch 7/20\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.4578 - acc: 0.8027 - val_loss: 0.4364 - val_acc: 0.7600\n",
      "Epoch 8/20\n",
      "740/740 [==============================] - 11s 14ms/step - loss: 0.3834 - acc: 0.8338 - val_loss: 0.3036 - val_acc: 0.9133\n",
      "Epoch 9/20\n",
      "740/740 [==============================] - 11s 14ms/step - loss: 0.3502 - acc: 0.8635 - val_loss: 0.2803 - val_acc: 0.9233\n",
      "Epoch 10/20\n",
      "740/740 [==============================] - 11s 14ms/step - loss: 0.2831 - acc: 0.9162 - val_loss: 0.2269 - val_acc: 0.9300\n",
      "Epoch 11/20\n",
      "740/740 [==============================] - 11s 14ms/step - loss: 0.2721 - acc: 0.9095 - val_loss: 0.2199 - val_acc: 0.8900\n",
      "Epoch 12/20\n",
      "740/740 [==============================] - 11s 14ms/step - loss: 0.2680 - acc: 0.9095 - val_loss: 0.2395 - val_acc: 0.9300\n",
      "Epoch 13/20\n",
      "740/740 [==============================] - 11s 14ms/step - loss: 0.2175 - acc: 0.9257 - val_loss: 0.1627 - val_acc: 0.9300\n",
      "Epoch 14/20\n",
      "740/740 [==============================] - 11s 14ms/step - loss: 0.1740 - acc: 0.9527 - val_loss: 0.1713 - val_acc: 0.9400\n",
      "Epoch 15/20\n",
      "740/740 [==============================] - 11s 14ms/step - loss: 0.1844 - acc: 0.9446 - val_loss: 0.1412 - val_acc: 0.9500\n",
      "Epoch 16/20\n",
      "740/740 [==============================] - 11s 14ms/step - loss: 0.1756 - acc: 0.9473 - val_loss: 0.1510 - val_acc: 0.9300\n",
      "Epoch 17/20\n",
      "740/740 [==============================] - 11s 14ms/step - loss: 0.1567 - acc: 0.9622 - val_loss: 0.2171 - val_acc: 0.9433\n",
      "Epoch 18/20\n",
      "740/740 [==============================] - 11s 14ms/step - loss: 0.1500 - acc: 0.9486 - val_loss: 0.1951 - val_acc: 0.9333\n",
      "Epoch 19/20\n",
      "740/740 [==============================] - 11s 14ms/step - loss: 0.1310 - acc: 0.9635 - val_loss: 0.1350 - val_acc: 0.9567\n",
      "Epoch 20/20\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.1513 - acc: 0.9459 - val_loss: 0.1929 - val_acc: 0.9233\n"
     ]
    }
   ],
   "source": [
    "model.compile(optimizer='adam',\n",
    "              loss = 'binary_crossentropy',\n",
    "              metrics = ['accuracy'])\n",
    "\n",
    "BATCH_SIZE = 32\n",
    "EPOCHS = 20\n",
    "\n",
    "history = model.fit(\n",
    "    X_train_norm, \n",
    "    y_train,  # prepared data\n",
    "    batch_size=BATCH_SIZE,\n",
    "    epochs=EPOCHS,\n",
    "    validation_data=(X_test_norm, y_test),\n",
    "    verbose=1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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B4ztde+7zqE/ILSplzvwtPPv5TsbHtWPZnNHEd2rl2viUauY0KaiqEudbxUMD\nrqt5mz6TAXFtK6SyUji8vkFFR/syzjDj5XV8uf0oj02O443bhhHRshGH+VaqmdCkoM5VVgLbF0Hf\nybVPBhPeHrpc6NqkcGybvT6hfpXMy7YeYcYrP5BTUMKHv7yY+8b21BZGSjWQJgV1rn2rIP9E9RXM\nlcVNhaOJkJPmmnPXs39CcamNZ5Yl8Zv5W4jv2IovfjOGkT2jXBOLUj5Kk4I6V+KHENrWamFUl7hp\n1s/dLhpOO+V7iOoN4R3q3PRoTgE3vbGed35M4e7RscyffTHtWwW7Jg6lfJgmBXVWfhbsXQEDZzo3\n7WZ0L4jua7VCOl9lpXBovVN3CT8kn2Dqi+vYc+wMr9w8lCenxROo8yEo5RL6n6TO2r4YbCUwxImi\no3JxU6xin4JT53fuY1uh+EytScFmM7yyOpnb/ruRqNAgls4ZzdRBOvS1Uq6kSUGdtXUedBgIHQY4\nv0/cNKuT275V53fuivqE6lse5RSUMPv9BJ5bsYdpgzrx6a9G0atd2PmdUylVhSYFZTm+C45sca6C\n2VGnoRDW4fyLkFLWQXQfq1VTJcnHz3D1Kz+wZk8mf57en3/fNITQFtoZXyl30P8sZUmcB34BMPCG\n+u3n52cVIW1dACWFENiAyt7y+oRBVc/99c4MHlqQSHCgH/NnX8yF3WtpJquUOm96p6Csi/K2hdBr\nAoS1rf/+cVOhJA8Orm3Y+Y9WrU+w2QwvfbOPe95PIDY6lGVzRmtCUKoR6J2CsibMyT1WvwpmR93H\nQFC41ZGtz5X13z/le+tnNysp5BWV8siirXy54xjXXtCZ/712IMGBOkuaUo1Bk4KyKphbtm7YBR0g\noAX0nmCNmmr7V9WhtuuSss5q2hrensMn85n9fgJ7M87wxNR+3D06VnsnK9WItPjI1xVkw67PYcD1\n1sW9oeKmQl4mpCXUb7+K8Y5Gs27fCaa/so6jOYW8d9dF/HJMD00ISjUyTQq+LmkJlBXBkFpGRHVG\n7wngF1j/VkhHt0JxLt8U9uH2uRtpHx7MsjmjGN07+vziUUo1iCYFX7d1PrSNs5qWno/gCIgdY9Ur\n1GOOhZL9VuX0owmtmBjfgU8euIRuUaHnF4tSqsE0Kfiyk/utKTUHzwJXFNPETYWs/XBir1ObH80p\nYNu6z9ln68ztEy7k1VuGav8DpTxMk4IvS5wH4geDbnTN8fpOsX46UYSUkJLF1S+uJa44ieDeY/nN\n5b3x89P6A6U8TZOCr7LZYNsC6DG+QbOcVatVJ2sKzzrmWJi38TCz3tzAsKBDhEohMUMnuub8Sqnz\n5takICKTRGSPiCSLyGPVrO9WOlsDAAAeF0lEQVQqIqtFZIuIbBORKe6MRzlI+R5yUhveN6EmfadA\n+mY4fbTKquJSG39asp0/LtnOJT2jeX5ErrWiW/0m1VFKuY/bkoKI+AOvAJOBeGCWiMRX2uwJYKEx\n5gLgJuBVd8WjKtk6H1pEWPUArlQ+x8Kes3MsGGPYcvgUt761kQ83Hua+sT2Ze8eFtEz7warkbkgv\naqWUW7izVm8EkGyMOQAgIh8BMwDHmd4NUD6zegRwxI3xqHJFubBzGQy8HgJbuvbYbftCm56w+wuS\nu81kaeIRliYe4XBWPiFB/rw46wKmD+5kTft5eMP5N4VVSrmUO5NCZyDV4XUacFGlbZ4BVorIr4FQ\n4IrqDiQis4HZAF27dnV5oD5n51JrrCJXFx0BR3IKOR46iv7753HNC1+RJyGM6hXNry/rxZUDOtAq\n2D55z5FEK4Z6zseslHIvdyaF6pqSVG7APgt4xxjzvIiMBN4XkQHGGNs5OxnzBvAGwPDhw51vBK+q\nt3U+tOkBMZVzdMNk5RWzfPtRliUe4aeULIZKDz5pUcpLF54gfuKdtAuvZuTUSuMdKaW8gzuTQhoQ\n4/C6C1WLh+4GJgEYY9aLSDAQDRx3Y1y+7dQh64I8/onz6puQV1TK17syWJp4hO/2ZlJqM/RsG8rD\nE/owfdAYeOcVxplNEH5/9QdIWQdt+2l9glJexp1JYRPQW0RigXSsiuTK5RWHgcuBd0SkHxAMZLox\nJrVtgfVzcP37JhSX2vh+XyZLE4+wamcGBSVldIwI5u7RsUwf0on4jq3OjlXUdzLsWAKlRVXHVKqo\nT3B98ZVS6vy4LSkYY0pFZA6wAvAH5hpjkkTkWSDBGLMMeBh4U0R+i1W0dIcx9RgjQdWPMVaHte5j\nILJ+dTNf7TjK459s51R+CZEhgVwztDMzBnfiwu5tqu90FjcNfn7PuivpVamq6MgWrU9Qyku5dUwB\nY8xyYHmlZU85PN8JjHJnDMrB4Q1w6iCM/UO9dvtw4yGe+HQHg7pE8s8bejGmd1uCAupozRw7FgJD\nrY5slZNCRX2C/uqV8jY60ExTUFYC6T9D5m5r3oPQtvZHtDUQnbN1A1vnWRfqftOd2twYw0vfJvPC\nqr1cFteOV24eSssgJ+dKCAyGXpfD7uUw5Xlr2s5yWp+glNfSpOCNbDY4ngQH1lpTXB76EYpzq9/W\nP+hsgnBMFqFtIbTduckj6VOInwEtwpwIwfDnz5J4d/0hrr2gM3+/fhCB/vXs6xg3DXYts4qLugyz\nllXUJ9xSv2MppRqFJgVvYAxkHbCmxTz4nVW8kn/SWhfVyxqwLvZS6DQEis5Yk9nknbB+5h4/+zwv\nEzL3Qt5xKC2s/lxOdBYrLrXx8KKtfLb1CPeMieXxyf0aNlhdn4kg/tYAeeVJ4cgWKMm3htlWSnkd\nTQqecvqIlQAOrLV+nk6zlod3gt4TrTL52EshonP9j22MdWdRnjxyj1vP/QKsSuZa5BWVct8Hm/l+\n3wkenxzHvWN7NuDN2bVsbVUm7/4CrnjaWqb1CUp5NU0KjaWsBPZ8aRUHHVgLJ/dZy1u2sb41x/7O\nSgRRPc9/bgMRaBFuPdr0cHq3rLxi7nxnE9vTsvnH9YOYOTym7p3qEjcVvvwDnEiG6F5w8HtoF28V\naSmlvI5TSUFEPgbmAl9W7m2snFCcDwtugf3fQlAYdLsEhv3CSgLtB5xbCesh6dkF3PbfjaSfKuD1\n24YzIb69aw7cd4qVFPZ8AZH3W5P6XHCra46tlHI5Z+8UXgPuBF4UkUVYQ1Psdl9YzUjhaZh3I6Ru\ngGn/ggtuA/9AT0d1jn0ZZ7h97k/kFpXy3l0juKhHlOsOHhkDHQdbRUgxF1v1Cdo/QSmv5dRXVGPM\n18aYW4ChQAqwSkR+FJE7RcS7rnDeJD8L3psBaT/BdW/B8Lu8LiH8fPgUN7y+nlKbYcHska5NCOXi\npkHqT7DjY+u1jneklNdyutxCRKKAO4BfAluAf2MliVVuiaypyz0O70yDjB1w4wcw4DpPR1TF6j3H\nueXNjUS0DOTj+y4hvlOrundqiLipgIGE/0K7/hDqhsSjlHIJZ+sUPgHigPeBq4wx5dNqLRCRBHcF\n12TlpMN7060WRjcvhJ7jPR1RFZ9uSeeRRVvp0z6cd+8aQdvwFnXv1FDt4iGyG2Qf0qIjpbycs3cK\nLxtj4o0xf3VICAAYY4a7Ia6mK+sgvD3JulO49ROvTAhz1x3koQWJDO/emo/uvdi9CQGs1lDlM7Jp\nUlDKqzmbFPqJSGT5CxFpLSIPuCmmpitzD7w92epgdvtS6DbS0xGdwxjDcyt28+znO7myf3veuXPE\n2Ulv3G34nVZLpB7jGud8SqkGcTYp3GOMyS5/YYw5BdzjnpCaqKPb4O0pYCuDO5ZD56GejugcZTbD\n459s55XV+5k1IoZXbxlGcKCT4xi5QnRvmDUfgt1Ub6GUcglnm6T6iYiUD2stIv5AkPvCamLSEuCD\nayEoHH6xzOqA5mXe/P4AH21KZc74Xjw8sc/ZeQ+UUsqBs0lhBbBQRP6DNe/BfcBXbouqKTn4Pcy/\nyRp47hfL6j1PQWPIKSjhtTX7Gde3LY9c2dfT4SilvJizSeFR4F7gfqy5l1cCb7krqCZj39dWT+XI\nblYdQquOno6oWm9+d4CcghIemagJQSlVO6eSgn1oi9fsDwWw6zNYdCe06we3LfHasXwyzxQx94eD\nTBvUkQGdIzwdjlLKyznbT6E38FcgHmseZQCMMc6PttacbF0An94PnYfBLYugZWTd+3jIK6uTKSq1\n8bDeJSilnOBs66O3se4SSoHxwHtYHdl8T8LbsORea1C725Z4dUJIzcrnw42HmDm8C7HRoZ4ORynV\nBDibFFoaY74BxBhzyBjzDHCZ+8LyUutfgc8fgt4TrDsEJ2Yw86T/+3ofIsJvLu/t6VCUUk2EsxXN\nhSLiB+wTkTlAOtDOfWF5oR2fwIo/WvMbX/dfCPDuFrn7Ms6wZEsad4+OpWNES0+Ho5RqIpy9U3gI\nCAF+AwwDbgV+4a6gvFLCXGjTE65/2+sTAsDzK/cSEhTA/eN6eToUpVQTUmdSsHdUm2mMyTXGpBlj\n7jTGXGeM2dAI8XmH7FRrGsnBN4G/909WtzU1m6+SjvHLMbG0CfX+BKaU8h51JgVjTBkwTHy5C+z2\nRdbPgTd4Ng4nPbdiD21Cg/jlGN9sHKaUajhnv/ZuAZbaZ13LK19ojPnELVF5E2Ng2wKIuQjaxHo6\nmjr9mHyCdckneGJqP8JaeP9djVLKuzh71WgDnOTcFkcGaP5J4dh2yNwNU1/wdCR1Msbw9xV76BQR\nzK0Xd/N0OEqpJsjZHs13ujsQr7VtAfgFQv9rPB1JnVbuzGBrajZ/v25g446AqpRqNpzt0fw21p3B\nOYwxd7k8Im9iK7PqE3pPhJA2no6mVmU2w/Mr99AjOpTrhnbxdDhKqSbK2eKjzx2eBwPXAEdcH46X\nObgWcjNg0ExPR1KnpYnp7M3I5eWbLyDA3+mpt5VS6hzOFh997PhaROYDX7slIm+ybSG0iIA+kzwd\nSa2KS2386+u99O/UiikDvHOkVqVU09DQr5S9Ae+bOMCVivNg5zLoPwMCg+ve3oM+2nSY1KwCfn9l\nX/z8fLflsFLq/Dlbp3CGc+sUjmHNsdB87V4OJXkw6EZPR1Kr/OJSXvwmmRGxbRjbp62nw1FKNXHO\nFh+FuzsQr7NtAUTEQNdLPB1Jrd7+IYUTuUW8fttQnWJTKXXenCo+EpFrRCTC4XWkiFztxH6TRGSP\niCSLyGPVrP+XiCTaH3tFJLt+4btJ7nHY/63Vg9nPeyttc/JLeH3tfi6Pa8ewbt7dOkop1TQ4e8V7\n2hiTU/7CGJMNPF3bDvYxk14BJmNNzjNLROIdtzHG/NYYM8QYMwR4CW/pDLfjYzBlXl909Pp3+zld\nWKoT6CilXMbZpFDddnUVPY0Ako0xB4wxxcBHwIxatp8FzHcyHvfatgA6DIJ2cZ6OpEbHzxTy9g8p\nTB/cifhOrTwdjlKqmXA2KSSIyAsi0lNEeojIv4DNdezTGUh1eJ1mX1aFiHQDYoFva1g/W0QSRCQh\nMzPTyZAbKHMvHNni9XcJL3+bTEmZjd9N6OPpUJRSzYizSeHXQDGwAFgIFAC/qmOf6mo9q/SKtrsJ\nWGwfkbXqTsa8YYwZbowZ3ratm1vYbF8I4gcDr3fvec5DalY+8386zMwLY+iu02wqpVzI2dZHeUCV\niuI6pAExDq+7UHMv6JuoO8m4n81mFR31GAfhHTwdTY3+tWovfiL85jKdZlMp5VrOtj5aJSKRDq9b\ni8iKOnbbBPQWkVgRCcK68C+r5th9gdbAeufDdpPUjZB92KuLjvZmnGFJYjp3XNKdDhHe3alOKdX0\nOFt8FG1vcQSAMeYUdczRbIwpBeYAK4BdwEJjTJKIPCsi0x02nQV8ZIypqWip8WxbAIEhEDfN05HU\n6J8r9hAWFMB9Y3t6OhSlVDPk7IB4NhHpaow5DCAi3am5fqCCMWY5sLzSsqcqvX7GyRjcq7QIkpZY\nCaFFmKejqdaWw6dYuTOD303oQ2udZlMp5QbOJoU/AetEZK399aXAbPeE5CH7VkJhtlcXHT23Yg9R\noUHcNdr7Z4BTSjVNThUfGWO+AoYDe7BaID2M1QKp+di2AELbWpXMXuj7fZn8uP8kvxrfS6fZVEq5\njbMD4v0SeBCrBVEicDFWxfBlte3XZBScgr0rYPjd4O99F9zSMht/+XwnMW1acsvFzXtwWqWUZzlb\n0fwgcCFwyBgzHrgAcHMvskaU9CmUFXvtZDofbjzM3oxc/jQlnhYBOs2mUsp9nE0KhcaYQgARaWGM\n2Q00nwF3ti2E6D7Q6QJPR1LFqbxiXli1l1G9oriyf3tPh6OUauacLStJs/dT+BRYJSKnaC7TcZ46\nBId/hMueAC8cevqFVXvJLSrlqWn9dWhspZTbOduj+Rr702dEZDUQAXzltqga0/ZF1s+BN3g2jmrs\nOnqaDzce4raLu9G3g+9NaaGUanz1rlU1xqyte6smwhir1VHXkdC6u6ejOYcxhmc/20mrloH8Vge9\nU0o1Eu+dQaYxHE2EE3u9sm/CVzuOsf7ASR6e0IfIEO2oppRqHL6dFLYtBP8g6F/nJHKNqrCkjP/3\nxS7iOoQza4Q2QVVKNR7fTQplpbB9MfSeCC1bezqac7z53QHSswt46qp4Avx991eklGp8vnvFObAG\n8o57XdHR0ZwCXl2zn8kDOnBJz2hPh6OU8jG+mxS2LYDgCOhzpacjOcffvtxNmTH8cUo/T4eilPJB\nvpkUinJh9+fQ/xoIaOHpaCokpGSxNPEI917ag5g2IZ4ORynlg3wzKez+AkryvaroqMxmeOazJDpG\nBHP/OJ0rQSnlGb6ZFLZ9BBFdIeZiT0dSYfHmVHakn+axyXGEBHnfoHxKKd/ge0nhzDGrknnQTPDz\njrd/urCE51bsYXi31kwf3MnT4SilfJh3XBUb046Pwdi8akTUl77Zx8m8Yp6+Ssc3Ukp5lu8lhW0L\noOMQaOsdg7zuz8zl7R9SmDkshoFdIjwdjlLKx/lWUji+G45u9aoK5r98vpOWgf78fpJ3JCmllG/z\nraSwbQGIPwy83tORALB693HW7MnkwSt6Ex3mPU1jlVK+y3eSgs1mDZPdczyEtfN0NBSXWlNs9mgb\nyu0ju3s6HKWUAnwpKRxeDzmpXlN09M6PBzlwIo8np8UTFOA7vwallHfznatRegIEhUHcVE9HQuaZ\nIl78Jpnxfdsyvq/n71qUUqqc7/SSGvUgDP0FBIV6OhKeW7GbotIynpwW7+lQlFLqHL5zpwDQMtLT\nEbAtLZtFm9O4c1QsPdqGeTocpZQ6h28lBQ8zxvDnz3YSFRrEnMt6eTocpZSqQpNCI1qaeITNh07x\nhyvjaBUc6OlwlFKqCt+pU/CQkjIbOQUlZOUV89cvdzGwcwTXD+vi6bCUUqpamhTqIfNMEVl5xWTn\nF5NdUEJOfgnZBcVk55dUfZ1fQk5BCblFpecc49VbhuLnp+MbKaW8kyYFJy1MSOUPi7dVuy7AT4gM\nCSSiZSCRIUF0aBVM3w7hRLYMIjIksGJd73bhxHdq1ciRK6WU8zQpOGlpYjpd24Tw6KQ4hwRgJYHQ\nIH8d3VQp1SxoUnBCTn4JGw5kce+lPZg6qKOnw1FKKbdxa+sjEZkkIntEJFlEHqthm5kislNEkkRk\nnjvjaahv92RQZjNM7N/B06EopZRbue1OQUT8gVeACUAasElElhljdjps0xt4HBhljDklIl455sPK\npAzat2rBoM4634FSqnlz553CCCDZGHPAGFMMfATMqLTNPcArxphTAMaY426Mp0EKS8pYuzeTCfHt\ntdWQUqrZc2dS6AykOrxOsy9z1AfoIyI/iMgGEZlU3YFEZLaIJIhIQmZmppvCrd4PySfILy5jYrwW\nHSmlmj93JoXqvlabSq8DgN7AOGAW8JaIVBmgyBjzhjFmuDFmeNu2bV0eaG1WJmUQ3iKAi3tENep5\nlVLKE9yZFNKAGIfXXYAj1Wyz1BhTYow5COzBShJeocxm+HpXBuPj2umcB0opn+DOK90moLeIxIpI\nEHATsKzSNp8C4wFEJBqrOOmAG2Oql58Pn+JkXjET+7f3dChKKdUo3JYUjDGlwBxgBbALWGiMSRKR\nZ0Vkun2zFcBJEdkJrAZ+b4w56a6Y6mtl0jGC/P0Y26dxi6yUUspT3Np5zRizHFheadlTDs8N8Dv7\nw6sYY1i5M4NLekURriOaKqV8hBaU12BvRi6HTuZrqyOllE/RpFCDlUnHEIEr4r2yP51SSrmFJoUa\nrNyZwQUxkbQLD/Z0KEop1Wg0KVTjSHYB29NzdKwjpZTP0aRQjVU7MwCYGK9NUZVSvkWTQjVW7jxG\nr3Zh9Ggb5ulQlFKqUWlSqKR87gS9S1BK+SJNCpXo3AlKKV+mSaGSlUkZdGgVrHMnKKV8kiYFBzp3\nglLK12lScFAxd4IOgKeU8lGaFBysTMogPDiAi2J17gSllG/SpGBXPnfCZTp3glLKh+nVz65i7gQd\nAE8p5cM0KdhVzJ3QV+dOUEr5Lk0KnJ07YVSvKMJauHWKCaWU8mqaFHCYO0E7rCmlfJwmBc7OnXB5\nP507QSnl2zQpYM2dMLRra507QSnl83w+KVTMnaAD4CmllCaFirkTtD5BKaU0KazceYze7cKIjQ71\ndChKKeVxPp0UKuZO0LGOlFIK8PGkUDF3gvZiVkopwMeTQvncCQN17gSllAJ8OCno3AlKKVWVzyYF\nnTtBKaWq8tmkoHMnKKVUVT6ZFHTuBKWUqp5PXhE3H9K5E5RSqjo+mRR07gSllKqezyUFnTtBKaVq\n5takICKTRGSPiCSLyGPVrL9DRDJFJNH++KU74wHYk3GGw1k6d4JSSlXHbV+VRcQfeAWYAKQBm0Rk\nmTFmZ6VNFxhj5rgrjspWJmXo3AlKKVUDd94pjACSjTEHjDHFwEfADDeezykrdx7TuROUUqoG7kwK\nnYFUh9dp9mWVXSci20RksYjEVHcgEZktIgkikpCZmdnggNKzC9iRflrnTlBKqRq4MylUN3aEqfT6\nM6C7MWYQ8DXwbnUHMsa8YYwZbowZ3rZtw1sMrUo6BujcCUopVRN3JoU0wPGbfxfgiOMGxpiTxpgi\n+8s3gWFujIeVOzN07gSllKqFO5PCJqC3iMSKSBBwE7DMcQMR6ejwcjqwy13BZOcXs/Ggzp2glFK1\ncVvrI2NMqYjMAVYA/sBcY0ySiDwLJBhjlgG/EZHpQCmQBdzhrni+3X1c505QSqk6uLX3ljFmObC8\n0rKnHJ4/DjzuzhjKhQcHMiG+vc6doJRStfCZLr0T4tszQVsdKaVUrXxumAullFI106SglFKqgiYF\npZRSFTQpKKWUqqBJQSmlVAVNCkoppSpoUlBKKVVBk4JSSqkKYkzlgUu9m4hkAocauHs0cMKF4bia\nxnd+NL7z5+0xanwN180YU+cw000uKZwPEUkwxgz3dBw10fjOj8Z3/rw9Ro3P/bT4SCmlVAVNCkop\npSr4WlJ4w9MB1EHjOz8a3/nz9hg1PjfzqToFpZRStfO1OwWllFK10KSglFKqQrNMCiIySUT2iEiy\niDxWzfoWIrLAvn6jiHRvxNhiRGS1iOwSkSQRebCabcaJSI6IJNofT1V3LDfGmCIi2+3nTqhmvYjI\ni/bPb5uIDG3E2Po6fC6JInJaRB6qtE2jf34iMldEjovIDodlbURklYjss/9sXcO+v7Bvs09EftFI\nsT0nIrvtv78lIhJZw761/i24OcZnRCTd4fc4pYZ9a/1/d2N8CxxiSxGRxBr2bZTP0GWMMc3qgTUf\n9H6gBxAEbAXiK23zAPAf+/ObgAWNGF9HYKj9eTiwt5r4xgGfe/AzTAGia1k/BfgSEOBiYKMHf9fH\nsDrlePTzAy4FhgI7HJb9A3jM/vwx4O/V7NcGOGD/2dr+vHUjxDYRCLA//3t1sTnzt+DmGJ8BHnHi\nb6DW/3d3xVdp/fPAU578DF31aI53CiOAZGPMAWNMMfARMKPSNjOAd+3PFwOXi4g0RnDGmKPGmJ/t\nz88Au4DOjXFuF5oBvGcsG4BIEenogTguB/YbYxraw91ljDHfAVmVFjv+nb0LXF3NrlcCq4wxWcaY\nU8AqYJK7YzPGrDTGlNpfbgC6uPKc9VXD5+cMZ/7fz1tt8dmvHTOB+a4+ryc0x6TQGUh1eJ1G1Ytu\nxTb2f4wcIKpRonNgL7a6ANhYzeqRIrJVRL4Ukf6NGhgYYKWIbBaR2dWsd+Yzbgw3UfM/oic/v3Lt\njTFHwfoyALSrZhtv+Czvwrrzq05dfwvuNsdexDW3huI3b/j8xgAZxph9Naz39GdYL80xKVT3jb9y\nu1tntnErEQkDPgYeMsacrrT6Z6wikcHAS8CnjRkbMMoYMxSYDPxKRC6ttN4bPr8gYDqwqJrVnv78\n6sOjn6WI/AkoBT6sYZO6/hbc6TWgJzAEOIpVRFOZx/8WgVnUfpfgyc+w3ppjUkgDYhxedwGO1LSN\niAQAETTs1rVBRCQQKyF8aIz5pPJ6Y8xpY0yu/flyIFBEohsrPmPMEfvP48ASrFt0R858xu42GfjZ\nGJNReYWnPz8HGeXFavafx6vZxmOfpb1Sexpwi7EXflfmxN+C2xhjMowxZcYYG/BmDef26N+i/fpx\nLbCgpm08+Rk2RHNMCpuA3iISa/82eROwrNI2y4DyVh7XA9/W9E/havbyx/8Cu4wxL9SwTYfyOg4R\nGYH1ezrZSPGFikh4+XOsCskdlTZbBtxub4V0MZBTXkzSiGr8dubJz68Sx7+zXwBLq9lmBTBRRFrb\ni0cm2pe5lYhMAh4Fphtj8mvYxpm/BXfG6FhPdU0N53bm/92drgB2G2PSqlvp6c+wQTxd0+2OB1br\nmL1YrRL+ZF/2LNY/AEAwVrFDMvAT0KMRYxuNdXu7DUi0P6YA9wH32beZAyRhtaTYAFzSiPH1sJ93\nqz2G8s/PMT4BXrF/vtuB4Y38+w3BushHOCzz6OeHlaCOAiVY317vxqqn+gbYZ//Zxr7tcOAth33v\nsv8tJgN3NlJsyVhl8eV/g+Wt8ToBy2v7W2jEz+99+9/XNqwLfcfKMdpfV/l/b4z47MvfKf+7c9jW\nI5+hqx46zIVSSqkKzbH4SCmlVANpUlBKKVVBk4JSSqkKmhSUUkpV0KSglFKqgiYFpRqRfQTXzz0d\nh1I10aSglFKqgiYFpaohIreKyE/2MfBfFxF/EckVkedF5GcR+UZE2tq3HSIiGxzmJmhtX95LRL62\nD8z3s4j0tB8+TEQW2+cz+LCxRuhVyhmaFJSqRET6ATdiDWQ2BCgDbgFCscZbGgqsBZ627/Ie8Kgx\nZhBWD9zy5R8CrxhrYL5LsHrEgjUy7kNAPFaP11Fuf1NKOSnA0wEo5YUuB4YBm+xf4ltiDWZn4+zA\nZx8An4hIBBBpjFlrX/4usMg+3k1nY8wSAGNMIYD9eD8Z+1g59tm6ugPr3P+2lKqbJgWlqhLgXWPM\n4+csFHmy0na1jRFTW5FQkcPzMvT/UHkRLT5SqqpvgOtFpB1UzLXcDev/5Xr7NjcD64wxOcApERlj\nX34bsNZYc2SkicjV9mO0EJGQRn0XSjWAfkNRqhJjzE4ReQJrtiw/rJExfwXkAf1FZDPWbH032nf5\nBfAf+0X/AHCnffltwOsi8qz9GDc04ttQqkF0lFSlnCQiucaYME/HoZQ7afGRUkqpCnqnoJRSqoLe\nKSillKqgSUEppVQFTQpKKaUqaFJQSilVQZOCUkqpCv8f1OFgVItLHNsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8e33837588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_train_val_accuracy(history.history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bvvVP6HmV1c/BVTFjwdvPSjalTBvWieiIIO7/dDXpOflufCNKKVeUmxSMMSHG\nmIZOHiHGmIblbevo9TwDGAZ0ASaISBcn5UKAO4FllX8byjZFE/Kk7oHZV8Mr/WHvcmt01UmfQ6Oo\nitcZFA6dRsCa2dbsbyVX+fnw7LieHDiexb+/3uCmN6GUcpWddxD1BbYZY3YYY3KBT7DGTirtCeBp\nQMdSrqnaDIA+f4MdP0PTbnDL7xB3XcXODkqLnQhZx2DTN6es6t0mjJvOb8cnK/by46ZDld+HUqrC\n7EwKLYG9JV4nOZYVE5FYoJUx5uvyKhKRG0UkXkTik5OT3R+pOr2L/wPXfAmTv4HwtlWv74wLoGHU\nSX0WSrp7UHs6NQvhgXlrOZaRW/X9KaVcYmdScPYzsnhAHRHxAp4D7jtdRcaYmcaYOGNMXOPGjd0Y\nonKZj791IPdy01fGy9tqi9j+o9VOUYq/jzfTx/UkNTOXR75c5559KqVOy86kkAS0KvE6Cthf4nUI\n0A34WUR2Af2A+drYXI/0vAowkPix09VdWjTk7kEd+HrNAb5avd9pGaWUe9mZFFYA7UWkrYj4AeOB\n+UUrjTHHjTGRxphoY0w0sBQYaYyJtzEmVZOEt4XocyHxg+JB8kq76bwziG0dyiNfruPwCW12Uspu\ntiUFY0w+cDuwENgIzDHGrBeRx0VkpF37VbVM7CSrJ/Tu352u9vH24tkrepCdV8AD89bo3AtK2czW\n8YuMMQuMMR2MMe2MMU86lj1qjJnvpOwFepZQD3UZCf6NymxwBjijcQMeHNaZnzYnM+71P9l2OL0a\nA1SqftFB7ZRn+QZCzBhr4L3s42UWu6Z/G6aP68H25HSGv/grM37aRn6BDrWtlLtpUlCeFzsR8rNO\nGSSvJBFhdK8ovr/nfAZ1bsIzCzdz2Su/s2H/aTvWK6UqQJOC8rwWvaBJl3IvIRVpHOLPK1f35tWr\ne3HweA4jX/6NZxdtJidfR1ZVyh00KSjPE7HOFvYlwCHXhrYYFtOcxfeex8ieLXjpx22MePE3Vu05\nZnOgStV9mhRUzdD9SvDycTpIXllCg/yYPq4nb0/pQ3pOPmNe/YN/f72BrFw9a1CqsjQpqJohONKa\nqW31J5BfsWEtBnZswqJ7zmNC39a8+dtOhr6wRIfeVqqSNCmomiN2EmQega0LK7xpSIAvT14ew8c3\n9ANgwhtLeejztaRl57k7SqXqNE0KquZodxGENHepwbks/dtF8N1d5/G3c9ry0fI9XPzcEn7afPj0\nGyqlAE0Kqibx9oEeE2DrIjhxoNLVBPp58/CILsy7ZQBB/j5MeXsF985OJDVTR1tV6nQ0KaiaJXYi\nmEJY7XyQvIro1TqMb+48hzsv1kLjAAAdhUlEQVQuPJMvV+9n8HNL2JGsvaGVKo8mBVWzRLSD1gOs\nS0im6uMc+ft4c9+Qjnx529nkFxRy64cryc7Tu5OUKosmBVXzxE6ElO2wZ6nbquzWshHTr+zJpoNp\n/PPL9W6rV6m6RpOCqnm6jAK/BlVqcHZmYMcm3DawHbPj9zI34dSJfZRSmhRUTeTfALpeDus/h5w0\nt1Z9z6AO9DsjnIe/WMvmg+6tW6m6QJOCqpliJ0FeBqz/wq3V+nh78eL4WBr4+3Lrhwlk5OS7tX6l\najtNCqpmatUXItq7/RISQJOGAbw4oSc7j2Twj8/XYtzQoK1UXaFJQdVMItBrEuxdCslb3F79gHaR\n3DOoA18m7uej5XvcXr9StZUmBVVzdR8P4m3N4WyD2waeyXkdGvPY/A2s21f2BD9K1SeaFFTNFdIU\nOlwMiR9DgfvHMPLyEp6/sifhwX7c+uFKTug4SUppUlA1XOxEyDgM2xbbUn14sB8zro5lf2oWUz9d\nre0Lqt7TpKBqtvZDILixLQ3ORXq3CeeBoZ1YuP4Qs37fZdt+lKoNNCmoms3bF3qMhy3fQbp9o53+\n7dy2DO7SlP8u2MhKncFN1WOaFFTN13MiFObDmtm27UJE+L+xPWgeGsDtH67kWIaOqKrqJ1uTgogM\nFZHNIrJNRKY5WX+ziKwVkUQR+U1EutgZj6qlmnSCqD6w8n23DJJXlkZBvsy4qhdH0nO5d04ihYXa\nvqDqH9uSgoh4AzOAYUAXYIKTg/5HxpgYY0xP4Glgul3xqFqu17VwZDPMPB/WzYMCe3oid48K5ZER\nnflpczKv/rLdln0oVZPZeabQF9hmjNlhjMkFPgFGlSxgjDlR4mUwoD/NlHM9r4ZLX4DcDJh7HbzU\nC5bNtF672cR+bbi0RwueXbSZpTt0rmdVv9iZFFoCe0u8TnIsO4mI3CYi27HOFO50VpGI3Cgi8SIS\nn5ycbEuwqobz8oLek+G25XDlB9CgCXw7FZ7rBj/9FzKOuG1XIsJ/R8cQHRHMHR+vIjktx211K1XT\n2ZkUxMmyU84EjDEzjDHtgAeAh51VZIyZaYyJM8bENW7c2M1hqlrFyxs6XwrXfw9TvoNWZ8EvT1nJ\n4Zv7IGWHW3bTwN+HVyb24kRWHnd9sooCbV9Q9YSdSSEJaFXidRSwv5zynwCX2RiPqktEoE1/uOoT\nuHUZxIyBhHfhpd7w6WTYt7LKu+jUrCFPXNaNP7Yf5YXF7h9/SamayM6ksAJoLyJtRcQPGA/ML1lA\nRNqXeHkJsNXGeFRd1aQTjJoBd6+FAXfAth/gjYHwzgjYurhKdyyNi2vF2N5RvPTTNn7ZopcuVd0n\ndnbrF5HhwPOANzDLGPOkiDwOxBtj5ovIC8AgIA84BtxujCl3rsS4uDgTHx9vW8yqDsg+AQnvwNJX\nIO0ANOkKZ98J3cZYneEqKCu3gMtm/M6htGzGxbUitlUoPVuH0rxRoPtjV8omIpJgjIk7bbnaNtaL\nJgXlsvxcWDcXfn8RkjdCo9bW5aamXStc1Y7kdKbNW0vi3lRyCwoBaNYwgNjWofRsFUps6zBiWjYi\n0M/b3e9CKbfQpKBUkcJC2PY9fHW31TN6yrcQeWalqsrJL2DjgTRW7TlG4t5UVu1JZU9KJgDeXkKn\nZiGORBFGbOtQ2kYE4+Xl7J4LpaqXJgWlSkveAm8PAx9/KzGEtXFLtUfTc4oTROJe65HumOazYYAP\nPVuHEdsqlO5RjWjaMIDwYD/Cg/0I8NWzClV9NCko5czBtfDOJRAYbiWGhs3dvovCQsP25HRW7Ull\n1d5jrNqTypZDaZS+qzXIz7s4QYQH+xEeZP0bFuxHhJN/Gwb46lmHqjRNCkqVJSke3hsFjaJg8jcQ\nHGn7LtNz8tl88ATJabkcy8wlJcN6HMvI5WiGtexouvVvZm6B0zqC/bz5vyt6MCzG/YlM1X2aFJQq\nz67f4IMxENkBrv0KAkM9HVGx7LyC4qRR8jF/9X7W7jvOi+NjuaS7JgZVMZoUlDqdrYvh4/HQIhYm\nfQ7+DTwdUbnSc/KZPGs5q/am8sL4nozo3sLTIalaxNWkoPMpqPqr/SC44m3Yl2Alh7wsT0dUrgb+\nPrxzXV96tw7jrk8S+Wp1eQMEKFU5mhRU/db5UrjsVety0pxrrb4NNVgDfx/entKH3m3CuOuTVXyZ\nuM/TIak6RpOCUj2uhBHPwdaF8NkNts3V4C7B/j68M6UPfduGc8/sRE0Myq00KSgFEDcFhjwJG76A\n+XdYHd5qsCA/H2ZN7sNZbSO4Z3Yin69K8nRIqo7QpKBUkQG3wwX/gNUfWXM11PCbMIoSQ78zIrh3\nzmrmJWhiUFWnSUGpks7/Owy4E1a8Cd8/WuMTQ6CfN29d24ez20Vy/9zVzNXEoKpIk4JSJYnA4Mch\n7nr440VY8oxn4yksgC2LYPYk+F807Pr9lCKBft68eW0c55wZydS5q5kTv/fUepRykY+nA1CqxhGB\n4f8HeZnw05PgG2RdWqpOKTtg1QeQ+DGk7YegCPD2gy9vhVv+AL/gk4oH+HrzxjVx3PBePA/MWwMG\nxvVpVUblSpVNk4JSznh5wciXrcSw6CHrIBw3xd595mbCxvlWMtj1K4gXnDkIhv0POgyFpOXWuE0/\nPAHDnjpl86LEcNP7Cfx93hoKjWF839b2xqzqHE0KSpXF2wdGv2l1avv6HuuMoceV7t2HMbB/pZUI\n1s6FnBMQ1hYufAR6TIBGLf8qG30O9L0Rlr0GXUZZ05GWEuDrzeuTenPzBwlM+2wthQauOksTg3Kd\nDnOh1OnkZcGHV1i/3oObQHhbCD/DOniXfB4Ubl16ckVmCqyZDSvfh8PrwSfAOtDHToI2Z1tnKs7k\npMOrA8DLB27+DfyCnBfLL+CWD1by46bDPHl5N64+yz3DhKvaS8c+UsqdctIh4W1I3gzHdlnX/E+U\n6jTm39BKEqWTRXhbCGkBGNjxk5UINi+Aglxo0QtiJ0LMWAho5FosO36B90ZC/9vh4ifLDjm/gFs/\nWMkPmw7zxGXdmNRPE0N9pklBKbvlZUPqbitBpOyEYzv/ep6625rlrYi3v9UukZVizeXQ/UroNalS\nU4MC1uWs+LfhuoXQ+qwyi+XkF3DbhytZvPEwT4zqyqT+0ZXbn6r1NCko5UkF+XAi6eRkkXEUOgyB\njsOt2d+qIicNXulvXXa6+VfwDSyzaG5+Ibd9tJLvNxzivsEduP3CMxFXL3PVFYfWw0fjoVVf6HcL\nRJ322FjnaFJQqq7b/hO8f5nV2W7IE+UWzc0vZNq8NXy2ah9jekXx39Ex+PnUk25K2Sdg5gXWWVph\ngdWY37I3nHWL1Y7j4+fpCKuFDp2tVF3XbiD0uhb+fNmaTa4cfj5ePDuuB/cM6sC8lUlMemsZqZk1\ne0RYtzAGvrzNagca/xHcuwGGPQNZqfDZ3+D5GPjlaUhP9nSkNYatSUFEhorIZhHZJiLTnKy/V0Q2\niMgaEflBRLQlTKmKGPJvqxH7i1utNo5yiAh3DWrPC+N7smpPKqNf+YNdRzKqKVAPWfqK1fdj0L+g\nzQDwD4GzboTb4+HquVabzk9PwnNd4PNb4MBqT0fscbYlBRHxBmYAw4AuwAQR6VKq2CogzhjTHZgL\nPG1XPErVSQENYeQLcGQz/HJqhzZnRvVsyUc3nMWxzFwuf+V3VuxKsTlID9mz1Bq/qtMIGHDHyeu8\nvKD9YJj0Gdy2HHpdAxu+hNfPg1lDYf0XNX4IdbvYeabQF9hmjNlhjMkFPgFGlSxgjPnJGJPpeLkU\niLIxHqXqpjMHWbe1/v6CNYucC+Kiw/n81rMJC/Lj6jeW8cWqOjYnQ3oyfDoZQlvDZa+U33+kcUe4\n5Fnr0tKQJ61bjT+9Fl7oAb89Z/UpqUfsTAotgZIjcyU5lpXleuBbZytE5EYRiReR+ORkvfan1CmG\nPAkNmsEXt0F+jkubREcG89mtA+jVJpS7Zyfy/OIt1LYbT5wqLIB510HWMRj3nuv9PwJDrTGu7kyE\nKz+0+pcs/hdM7wJf3QWHNtgadk1hZ1JwlpqdfuNEZCIQBzgdktIYM9MYE2eMiWvcuLEbQ1SqjggM\nhUtfgOSNFRrZNTTIj/euO4sxvaJ4fvFW7p2zmpz8AhsDrQY/PQk7l8Al06FZTMW39/KGziNg8tdw\n8+9Wx8LVn8Cr/WHZTPfH66rEj6xOlDazMykkASWHaYwCTplpXEQGAQ8BI40xrv3EUUqdqsMQ6HEV\n/Dod9ie6vJmfjxf/d0V3pl7ckc9X7WPSm8s5llFL70za/B38+qw1XEjs1VWvr1k3GPUy3LMBOgyD\n7x6whjKvbsvfgC9useb5sJmdSWEF0F5E2oqIHzAemF+ygIjEAq9jJYTDNsaiVP0w9D8Q3Ni6DTPf\n9QO7iHDbwDN5aUIsiUmpXP7K7+xItv9XqVsd2wWf32idHQx38zwYwREw9i1o2g3mXmd1hqsum76B\nb/9udXos3WBuA9uSgjEmH7gdWAhsBOYYY9aLyOMiMtJR7BmgAfCpiCSKyPwyqlNKuSIwDC59Hg6t\ns34xV9ClPVrw8Q1ncSI7n9Gv/sHSHUerFE5adh5/bj/KzCXbmfrpaqZ/v4UFaw+w7XA6+QVunAc7\nLxvmXGNdoB73frk9vCvNLxgmfAL+Daze0enV8Ds2KR7mXg8tYmHMW9alLZtpj2al6qLPboR18+CG\nn6B59wpvvudoJlPeWc6elEyeGt2dMb1Pf2NgVm4BGw4cZ/Xe46zdd5w1SansOJJRPKNpRLAfxzJz\nKXS89vP2ol2TBnRqFkKHpiF0bNaADk1DaBkaWPFhOL66CxLegfEfQ6fhFdu2ovavglnDrEtL135l\nTwICa2iUNwdbSej6xdCgau2pOsyFUvVZZgrMOAtCmlqJwdu3wlUcz8zjlg8T+GP7Ue688EzuGdyh\n+GCdk1/ApgNprNl3nDV7U1m77zhbDqUVH/CbhPjTPSqU7lGNiIlqREzLRkQ28Cc7r4Bth9PZfDCN\nLYfS2OT498DxvzreNfD3oUPTBnQsThYhdGwaQkSDMsaLSvwYvrgZzr4bBj9W4fdZKRvmw5xJ0G2M\n9Qve3WNJZRyFtwZbd1D9bTFEtKtylZoUlKrvNn4Ns6+GgQ/B+X+vVBW5+YU8/MVa5sQnMaRLUyJD\n/FmbdJxNB0+QV2AdO8KD/Yhp2YgeUY2IcSSCpg0DKrSf41l5bD2UxuZDaWw+6HgcSiM1M6+4TGQD\nf4Z0bcqYXlH0ah1qJahD6+GNi6wB7iZ9YU2MVF1+e866ZfX8aTDwQffVm5cF746Eg2usM5FWfd1S\nrSYFpZTVKLphPtz0S+WG6c4+gUnexM+//kLiho1873M+YVEdiWlpHfy7RzWq3OUeFxhjSE7PYcvB\ndDYdPMHqpON8v+Eg2XmFnBEZzJXdG3H9huvwyc+Em5ZYZ0XVqWhcpcQPrRn6ul9R9ToLC6y2kU3f\nwJXvQ+dLq16ngyYFpZR1GWJGX2gUBX/7oexf0nlZ1gRChzdafR0OOx7H955UzHj7IQPugHPvsxpe\nq1l6Tj4L1h5gXvxert33T4Z4xfNk46fp1n8Yw2KaEeRXzTMM5+daI9UmxVu/6suZ2+K0jIHvplnT\nrQ79H/S72X1xoklBKVVkw5fWr8+LHoX+d8DRbScf+A9vtOZ8MI67gbz9ILIDNO4ETTpDky7QpJM1\nUdAPj1nTiDZsac361uUy919Pd8WfM2DhP1gSfScPH76QPSmZBPl5M6xbc8b0bkm/thF4eVVTXJkp\n8MaF1hwXN/wIYZUc1/OPl2HRQ9DvNuvWYjfTpKCU+suca2HjV9YBvGhGOPG2GjCbdIbGnR0JoLM1\njWh5DdO7/4AFf4dDa6HtedZQ1E06Vc/7AGugu3cugQ5D4coPMMCKXceYl5DEN2sPkJ6TT8vQQEb3\nasmYXlFER1bDGc2RrfDmRdaItdcvdH1ojSLrP7fGauoyCsa+U/Yc3VWgSUEp9ZeMI7D4nxDcxPHL\nvzNEtq/8DHAF+dac1T8+AbkZ0PcmuOCBih8MKyo9GV4/15px7qZfTtlfVm4BizYcZG5CEr9tO4Ix\n0LtNGGN6RXFJ9+Y0Cqz4XVgu2/EzfDAGzrgAJsx2vdF795/w3iirL8I1X4JvxRrpXaVJQSllv4wj\n8MPjsPI9qyf14Met+adt+KVLYYF1/X7vcus2zdOMa3TweDafr9rHvJVJbDucjp+3F9GRQbQOD6JV\neBCtwko8Dw90T3tEwjtWn4m+N8FwF2YCSN5i3Xoa3BiuXwRB4VWPoQyaFJRS1WffSlgwFfbFQ1Rf\na5iJFj3dV39hIfz4uHUb6KgZ1lDhLjLGsCbpOAvWHmDHkQz2pmSyJyWTzNyTB/6LbOBPq/BAK1E4\nEkaU43XzRoF4u9pGsfAhaza84f8HfW8ou1z6YeuSU16WleTCost9D0t3pNChaYOy+2uchiYFpVT1\nKiyE1R/B9/+EzKMQNwUufKTiv34L8iB5kzUL2oE11v36B9dCbro10N2ol6scqjGGlIxc9qRksvdY\nFntTMouTxd5jmexPzaag8K9jo4+X0Co8iAs7NWFs7yg6N29YduWFBTB7Imz5Dq76FNoPOrVMbobV\nLpK8GSZ/Ay17Oa0qJSOXeQlJfLx8DzuOZPDgsE7cdH7lOrJpUlBKeUZWKvz8FCyfac0Md+Ej0Huy\n83F7cjOsDmgHVlsH/wOrrbuhChyD+fkGW5eJmneHFr2sHsQ+fra/hbyCQg6kZrP3mCNRpGSy5VAa\nv2xJJq/A0LVFQ8b2jmJUz5aEBzuJJyfdmsEtdbd1WahJ57/WFeTDJ1fBtu+tYTk6Dj1pU2MMy3am\n8NGyPXy37iC5BYX0bhPGhL6tuSSmOYF+lRv/SJOCUsqzDq237lLa/Rs0627NJw0lEsAaOLr1r1th\nA8Otg3+z7tC8h/UIP6NaBoFz1bGMXL5as5+5CUmsSTqOr7cwsKN19jCwUxN8vUu0pRxPsnpb+/jB\n3360xi4yBr6+x2qkH/EcxF1XXDwlI5fPVibx0fI97EjOICTAhzG9ohjftxWdmpVzZuIiTQpKKc8z\nBtZ/BgsfhrQS06k0bGkd9Jt1/ysRNIryTJ+HStp8MI15K5P4bOU+jqTnEBHsx6ieLRnbO4ouLRwH\n8X0r4e3h1nu8Zj4snWE1zJ9zDwz6F8YYlu9M4aPle/h2rXVW0Kt1KBP6tmZE9xaVPitwRpOCUqrm\nyEmHjfMhpJmVAIIjPR2R2+QXFLJkazJzE5JYvOEwuQWFdGledHmpBRG7v7XmfG7RC/avhJgrOHbx\ny8xbtZ+Pl+9hu+OsYHRsSyac1dotZwXOaFJQSqlqVnR5aV5CEquTjuPjJQzs1ISpQV/TYd1znGjW\nj8caPs5XG1LIzS8ktnUoV9lwVuCMJgWllPKgLYfSmJeQxGer9pGcls2FvutYlncmXv4hXN6rJRP6\nti7/LiY306SglFI1QH5BIb9uPcLijYfo0SqUEd2bV//AfbieFKo/MqWUqkd8vL0Y2KkJAzs18XQo\nLrFtjmallFK1jyYFpZRSxTQpKKWUKqZJQSmlVDFNCkoppYrZmhREZKiIbBaRbSIyzcn680RkpYjk\ni8hYO2NRSil1erYlBRHxBmYAw4AuwAQR6VKq2B5gMvCRXXEopZRynZ39FPoC24wxOwBE5BNgFLCh\nqIAxZpdjXaGNcSillHKRnUmhJbC3xOsk4KzKVCQiNwI3Ol6mi8jmSsYUCRyp5LbVQeOrGo2v6mp6\njBpf5bVxpZCdScHZGLiVGlPDGDMTmFm1cEBE4l3p5u0pGl/VaHxVV9Nj1PjsZ2dDcxLQqsTrKGB/\nGWWVUkrVAHYmhRVAexFpKyJ+wHhgvo37U0opVUW2JQVjTD5wO7AQ2AjMMcasF5HHRWQkgIj0EZEk\n4ArgdRFZb1c8DlW+BGUzja9qNL6qq+kxanw2q3VDZyullLKP9mhWSilVTJOCUkqpYnUyKbgwvIa/\niMx2rF8mItHVGFsrEflJRDaKyHoRuctJmQtE5LiIJDoej1ZXfI797xKRtY59nzLNnVhedHx+a0Sk\nVzXG1rHE55IoIidE5O5SZar98xORWSJyWETWlVgWLiLfi8hWx79hZWx7raPMVhG5tppie0ZENjn+\n/z4XkdAyti33u2BzjP8SkX0l/h+Hl7FtuX/vNsY3u0Rsu0QksYxtq+UzdBtjTJ16AN7AduAMwA9Y\nDXQpVeZW4DXH8/HA7GqMrznQy/E8BNjiJL4LgK89+BnuAiLLWT8c+BarL0o/YJkH/68PAm08/fkB\n5wG9gHUllj0NTHM8nwb8z8l24cAOx79hjudh1RDbEMDH8fx/zmJz5btgc4z/Au534TtQ7t+7XfGV\nWv8s8KgnP0N3PerimULx8BrGmFygaHiNkkYB7zqezwUuEhFnne3czhhzwBiz0vE8DevOrJbVsW83\nGgW8ZyxLgVARae6BOC4Cthtjdntg3ycxxiwBUkotLvk9exe4zMmmFwPfG2NSjDHHgO+BoXbHZoxZ\nZKw7BAGWYvUj8pgyPj9XuPL3XmXlxec4dowDPnb3fj2hLiYFZ8NrlD7oFpdx/GEcByKqJboSHJet\nYoFlTlb3F5HVIvKtiHSt1sCsnueLRCTBMcRIaa58xtVhPGX/IXry8yvS1BhzAKwfA4CzSXprwmd5\nHdaZnzOn+y7Y7XbHJa5ZZVx+qwmf37nAIWPM1jLWe/ozrJC6mBRcGV7DbUNwVJaINADmAXcbY06U\nWr0S65JID+Al4IvqjA042xjTC2uE29tE5LxS62vC5+cHjAQ+dbLa059fRXj0sxSRh4B84MMyipzu\nu2CnV4F2QE/gANYlmtI8/l0EJlD+WYInP8MKq4tJwZXhNYrLiIgP0IjKnbpWioj4YiWED40xn5Ve\nb4w5YYxJdzxfAPiKSGR1xWeM2e/49zDwOdYpekk1YQiTYcBKY8yh0is8/fmVcKjosprj38NOynjs\ns3Q0ao8ArjaOi9+lufBdsI0x5pAxpsAYUwi8Uca+PfpddBw/RgOzyyrjyc+wMupiUnBleI35QNFd\nHmOBH8v6o3A3x/XHt4CNxpjpZZRpVtTGISJ9sf6fjlZTfMEiElL0HKtBcl2pYvOBaxx3IfUDjhdd\nJqlGZf468+TnV0rJ79m1wJdOyiwEhohImOPyyBDHMluJyFDgAWCkMSazjDKufBfsjLFkO9XlZezb\n08PpDAI2GWOSnK309GdYKZ5u6bbjgXV3zBasuxIecix7HOsPACAA67LDNmA5cEY1xnYO1untGiDR\n8RgO3Azc7ChzO7Ae606KpcCAaozvDMd+VztiKPr8SsYnWBMobQfWAnHV/P8bhHWQb1RimUc/P6wE\ndQDIw/r1ej1WO9UPwFbHv+GOsnHAmyW2vc7xXdwGTKmm2LZhXYsv+g4W3Y3XAlhQ3nehGj+/9x3f\nrzVYB/rmpWN0vD7l77064nMsf6foe1eirEc+Q3c9dJgLpZRSxeri5SOllFKVpElBKaVUMU0KSiml\nimlSUEopVUyTglJKqWKaFJSqRo4RXL/2dBxKlUWTglJKqWKaFJRyQkQmishyxxj4r4uIt4iki8iz\nIrJSRH4QkcaOsj1FZGmJuQnCHMvPFJHFjoH5VopIO0f1DURkrmM+gw+ra4RepVyhSUGpUkSkM3Al\n1kBmPYEC4GogGGu8pV7AL8A/HZu8BzxgjOmO1QO3aPmHwAxjDcw3AKtHLFgj494NdMHq8Xq27W9K\nKRf5eDoApWqgi4DewArHj/hArMHsCvlr4LMPgM9EpBEQaoz5xbH8XeBTx3g3LY0xnwMYY7IBHPUt\nN46xchyzdUUDv9n/tpQ6PU0KSp1KgHeNMQ+etFDkkVLlyhsjprxLQjklnhegf4eqBtHLR0qd6gdg\nrIg0geK5lttg/b2MdZS5CvjNGHMcOCYi5zqWTwJ+MdYcGUkicpmjDn8RCarWd6FUJegvFKVKMcZs\nEJGHsWbL8sIaGfM2IAPoKiIJWLP1XenY5FrgNcdBfwcwxbF8EvC6iDzuqOOKanwbSlWKjpKqlItE\nJN0Y08DTcShlJ718pJRSqpieKSillCqmZwpKKaWKaVJQSilVTJOCUkqpYpoUlFJKFdOkoJRSqtj/\nAwOGb4uDRRIFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8e33e228d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_train_val_loss(history.history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model 4: Increasing accuracy with deeper network\n",
    "\n",
    "** Architecture **\n",
    "- Conv2D -> MaxPool -> Conv2D -> MaxPool -> Conv2D -> MaxPool -> Dense -> Dropout -> Dense -> Dropout -> (Dense -> Sigmoid)\n",
    "\n",
    "** Optimizer **\n",
    "\n",
    "- Adam\n",
    "- Batch size = 32\n",
    "- Epoch = 30 (change)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "K.clear_session()  # clear default graph\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Conv2D(filters=8, kernel_size=(3,3), strides=1,input_shape=image_shape))\n",
    "model.add(LeakyReLU(0.1))\n",
    "                            \n",
    "model.add(MaxPooling2D(pool_size=(3, 3)))\n",
    "\n",
    "model.add(Conv2D(filters=16, kernel_size=(3,3), strides=1, input_shape=image_shape))\n",
    "model.add(LeakyReLU(0.1))\n",
    "                            \n",
    "model.add(MaxPooling2D(pool_size=(3, 3)))\n",
    "\n",
    "model.add(Conv2D(filters=32, kernel_size=(3,3), strides=1, input_shape=image_shape))\n",
    "model.add(LeakyReLU(0.1))\n",
    "                            \n",
    "model.add(MaxPooling2D(pool_size=(3, 3)))\n",
    "\n",
    "model.add(Flatten())\n",
    "    \n",
    "model.add(Dense(32))\n",
    "model.add(LeakyReLU(0.1))\n",
    "model.add(Dropout(0.5))\n",
    "\n",
    "model.add(Dense(8))\n",
    "model.add(LeakyReLU(0.1))\n",
    "model.add(Dropout(0.5))\n",
    "\n",
    "model.add(Dense(1))\n",
    "model.add(Activation('sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 740 samples, validate on 300 samples\n",
      "Epoch 1/30\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.6968 - acc: 0.4919 - val_loss: 0.6908 - val_acc: 0.7400\n",
      "Epoch 2/30\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.6878 - acc: 0.5446 - val_loss: 0.6769 - val_acc: 0.6567\n",
      "Epoch 3/30\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.6649 - acc: 0.5662 - val_loss: 0.6062 - val_acc: 0.7467\n",
      "Epoch 4/30\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.6279 - acc: 0.6446 - val_loss: 0.5569 - val_acc: 0.7700\n",
      "Epoch 5/30\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.5898 - acc: 0.6824 - val_loss: 0.5602 - val_acc: 0.7167\n",
      "Epoch 6/30\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.5499 - acc: 0.7257 - val_loss: 0.4004 - val_acc: 0.8467\n",
      "Epoch 7/30\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.4833 - acc: 0.7784 - val_loss: 0.3304 - val_acc: 0.8967\n",
      "Epoch 8/30\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.3653 - acc: 0.8635 - val_loss: 0.3415 - val_acc: 0.8467\n",
      "Epoch 9/30\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.3853 - acc: 0.8608 - val_loss: 0.2478 - val_acc: 0.9200\n",
      "Epoch 10/30\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.2952 - acc: 0.8986 - val_loss: 0.1671 - val_acc: 0.9233\n",
      "Epoch 11/30\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.2404 - acc: 0.9324 - val_loss: 0.1398 - val_acc: 0.9467\n",
      "Epoch 12/30\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.1928 - acc: 0.9432 - val_loss: 0.3573 - val_acc: 0.8700\n",
      "Epoch 13/30\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.2474 - acc: 0.9041 - val_loss: 0.1480 - val_acc: 0.9367\n",
      "Epoch 14/30\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.1910 - acc: 0.9514 - val_loss: 0.1190 - val_acc: 0.9300\n",
      "Epoch 15/30\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.1418 - acc: 0.9703 - val_loss: 0.1339 - val_acc: 0.9433\n",
      "Epoch 16/30\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.1491 - acc: 0.9541 - val_loss: 0.1210 - val_acc: 0.9367\n",
      "Epoch 17/30\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.1378 - acc: 0.9527 - val_loss: 0.1052 - val_acc: 0.9467\n",
      "Epoch 18/30\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.1147 - acc: 0.9689 - val_loss: 0.1050 - val_acc: 0.9500\n",
      "Epoch 19/30\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.1097 - acc: 0.9757 - val_loss: 0.0899 - val_acc: 0.9567\n",
      "Epoch 20/30\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.1394 - acc: 0.9595 - val_loss: 0.1114 - val_acc: 0.9633\n",
      "Epoch 21/30\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.1342 - acc: 0.9581 - val_loss: 0.1188 - val_acc: 0.9500\n",
      "Epoch 22/30\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.0929 - acc: 0.9743 - val_loss: 0.1287 - val_acc: 0.9467\n",
      "Epoch 23/30\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.0819 - acc: 0.9824 - val_loss: 0.1009 - val_acc: 0.9433\n",
      "Epoch 24/30\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.0854 - acc: 0.9757 - val_loss: 0.0776 - val_acc: 0.9700\n",
      "Epoch 25/30\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.0629 - acc: 0.9784 - val_loss: 0.0882 - val_acc: 0.9533\n",
      "Epoch 26/30\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.0593 - acc: 0.9905 - val_loss: 0.1236 - val_acc: 0.9533\n",
      "Epoch 27/30\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.0535 - acc: 0.9824 - val_loss: 0.1740 - val_acc: 0.9533\n",
      "Epoch 28/30\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.0690 - acc: 0.9784 - val_loss: 0.2394 - val_acc: 0.9267\n",
      "Epoch 29/30\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.0856 - acc: 0.9743 - val_loss: 0.0756 - val_acc: 0.9633\n",
      "Epoch 30/30\n",
      "740/740 [==============================] - 11s 15ms/step - loss: 0.0561 - acc: 0.9892 - val_loss: 0.1535 - val_acc: 0.9600\n"
     ]
    }
   ],
   "source": [
    "model.compile(optimizer='adam',\n",
    "              loss = 'binary_crossentropy',\n",
    "              metrics = ['accuracy'])\n",
    "\n",
    "BATCH_SIZE = 32\n",
    "EPOCHS = 30\n",
    "\n",
    "history = model.fit(\n",
    "    X_train_norm, \n",
    "    y_train,  # prepared data\n",
    "    batch_size=BATCH_SIZE,\n",
    "    epochs=EPOCHS,\n",
    "    validation_data=(X_test_norm, y_test),\n",
    "    verbose=1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LtrPvSB7HTxbx3MIdnNWpBTMGVjDyWSkFaFJQ1ZWdDHuX2+521WzUzzx+kt9+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\ntgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8e33cac278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_train_val_accuracy(history.history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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H1GLqbGOMprIIdL/4Pbx8Fnz/DHROt3s6b/4C6iohKtFOXx08BdJPheBQ2PQ5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\nuQUbIb4HhEV5fv2khhlI7m2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e5+dPyhdmpLExr5QXFm1le2EFZw/tyvjBKSTENJM+IijY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\nSUGS1LMoSJJ6FgVJUs+iIEnqWRQkSb2faftADvBMzmIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8e42171978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_train_val_loss(history.history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model 5: Use pre-built networks via transfer learning\n",
    "\n",
    "** Architecture **\n",
    "- VGG16\n",
    "\n",
    "** Optimizer **\n",
    "\n",
    "- Adam\n",
    "- Batch size = 64\n",
    "- Epoch = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.applications import VGG16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading the pre-trained weights\n",
    "K.clear_session()\n",
    "\n",
    "conv_model = VGG16(weights='imagenet',\n",
    "                  include_top=False, # Download only the conv network while skip the last two fully connected layers\n",
    "                  input_shape=image_shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         (None, 256, 256, 3)       0         \n",
      "_________________________________________________________________\n",
      "block1_conv1 (Conv2D)        (None, 256, 256, 64)      1792      \n",
      "_________________________________________________________________\n",
      "block1_conv2 (Conv2D)        (None, 256, 256, 64)      36928     \n",
      "_________________________________________________________________\n",
      "block1_pool (MaxPooling2D)   (None, 128, 128, 64)      0         \n",
      "_________________________________________________________________\n",
      "block2_conv1 (Conv2D)        (None, 128, 128, 128)     73856     \n",
      "_________________________________________________________________\n",
      "block2_conv2 (Conv2D)        (None, 128, 128, 128)     147584    \n",
      "_________________________________________________________________\n",
      "block2_pool (MaxPooling2D)   (None, 64, 64, 128)       0         \n",
      "_________________________________________________________________\n",
      "block3_conv1 (Conv2D)        (None, 64, 64, 256)       295168    \n",
      "_________________________________________________________________\n",
      "block3_conv2 (Conv2D)        (None, 64, 64, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_conv3 (Conv2D)        (None, 64, 64, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_pool (MaxPooling2D)   (None, 32, 32, 256)       0         \n",
      "_________________________________________________________________\n",
      "block4_conv1 (Conv2D)        (None, 32, 32, 512)       1180160   \n",
      "_________________________________________________________________\n",
      "block4_conv2 (Conv2D)        (None, 32, 32, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_conv3 (Conv2D)        (None, 32, 32, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_pool (MaxPooling2D)   (None, 16, 16, 512)       0         \n",
      "_________________________________________________________________\n",
      "block5_conv1 (Conv2D)        (None, 16, 16, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv2 (Conv2D)        (None, 16, 16, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv3 (Conv2D)        (None, 16, 16, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_pool (MaxPooling2D)   (None, 8, 8, 512)         0         \n",
      "=================================================================\n",
      "Total params: 14,714,688\n",
      "Trainable params: 14,714,688\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "conv_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Freeze the layers \n",
    "for layer in conv_model.layers:\n",
    "    layer.trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:12: UserWarning: Update your `Model` call to the Keras 2 API: `Model(inputs=Tensor(\"in..., outputs=Tensor(\"de...)`\n",
      "  if sys.path[0] == '':\n"
     ]
    }
   ],
   "source": [
    "# Add the custom layers to the top of the network\n",
    "x = conv_model.output # this has a shape of (None, 8, 8, 512)\n",
    "\n",
    "x = Flatten()(x) # flatten the output from the conv net of vgg16\n",
    "x = Dense(64, activation = \"relu\")(x)\n",
    "#x = Dropout(0.5)(x)\n",
    "x = Dense(8, activation = \"relu\")(x)\n",
    "#x = Dropout(0.5)(x)\n",
    "\n",
    "outputs = Dense(1, activation = 'sigmoid')(x)\n",
    "\n",
    "vgg16_model = Model(input = conv_model.input, output = outputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         (None, 256, 256, 3)       0         \n",
      "_________________________________________________________________\n",
      "block1_conv1 (Conv2D)        (None, 256, 256, 64)      1792      \n",
      "_________________________________________________________________\n",
      "block1_conv2 (Conv2D)        (None, 256, 256, 64)      36928     \n",
      "_________________________________________________________________\n",
      "block1_pool (MaxPooling2D)   (None, 128, 128, 64)      0         \n",
      "_________________________________________________________________\n",
      "block2_conv1 (Conv2D)        (None, 128, 128, 128)     73856     \n",
      "_________________________________________________________________\n",
      "block2_conv2 (Conv2D)        (None, 128, 128, 128)     147584    \n",
      "_________________________________________________________________\n",
      "block2_pool (MaxPooling2D)   (None, 64, 64, 128)       0         \n",
      "_________________________________________________________________\n",
      "block3_conv1 (Conv2D)        (None, 64, 64, 256)       295168    \n",
      "_________________________________________________________________\n",
      "block3_conv2 (Conv2D)        (None, 64, 64, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_conv3 (Conv2D)        (None, 64, 64, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_pool (MaxPooling2D)   (None, 32, 32, 256)       0         \n",
      "_________________________________________________________________\n",
      "block4_conv1 (Conv2D)        (None, 32, 32, 512)       1180160   \n",
      "_________________________________________________________________\n",
      "block4_conv2 (Conv2D)        (None, 32, 32, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_conv3 (Conv2D)        (None, 32, 32, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_pool (MaxPooling2D)   (None, 16, 16, 512)       0         \n",
      "_________________________________________________________________\n",
      "block5_conv1 (Conv2D)        (None, 16, 16, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv2 (Conv2D)        (None, 16, 16, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv3 (Conv2D)        (None, 16, 16, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_pool (MaxPooling2D)   (None, 8, 8, 512)         0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 32768)             0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 64)                2097216   \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 8)                 520       \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 1)                 9         \n",
      "=================================================================\n",
      "Total params: 16,812,433\n",
      "Trainable params: 2,097,745\n",
      "Non-trainable params: 14,714,688\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "vgg16_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 740 samples, validate on 300 samples\n",
      "Epoch 1/20\n",
      "740/740 [==============================] - 238s 322ms/step - loss: 0.7190 - acc: 0.5446 - val_loss: 0.6169 - val_acc: 0.4800\n",
      "Epoch 2/20\n",
      "740/740 [==============================] - 238s 322ms/step - loss: 0.5510 - acc: 0.6554 - val_loss: 0.5596 - val_acc: 0.8300\n",
      "Epoch 3/20\n",
      "740/740 [==============================] - 238s 322ms/step - loss: 0.4873 - acc: 0.8149 - val_loss: 0.5314 - val_acc: 0.8633\n",
      "Epoch 4/20\n",
      "740/740 [==============================] - 239s 322ms/step - loss: 0.4729 - acc: 0.8365 - val_loss: 0.5163 - val_acc: 0.8833\n",
      "Epoch 5/20\n",
      "740/740 [==============================] - 238s 322ms/step - loss: 0.4307 - acc: 0.8838 - val_loss: 0.4223 - val_acc: 0.8733\n",
      "Epoch 6/20\n",
      "740/740 [==============================] - 239s 322ms/step - loss: 0.2694 - acc: 0.9162 - val_loss: 0.2870 - val_acc: 0.8900\n",
      "Epoch 7/20\n",
      "740/740 [==============================] - 239s 322ms/step - loss: 0.1924 - acc: 0.9324 - val_loss: 0.2468 - val_acc: 0.9033\n",
      "Epoch 8/20\n",
      "740/740 [==============================] - 239s 323ms/step - loss: 0.1386 - acc: 0.9500 - val_loss: 0.2421 - val_acc: 0.8833\n",
      "Epoch 9/20\n",
      "740/740 [==============================] - 241s 325ms/step - loss: 0.1143 - acc: 0.9622 - val_loss: 0.2546 - val_acc: 0.9100\n",
      "Epoch 10/20\n",
      "740/740 [==============================] - 240s 324ms/step - loss: 0.0865 - acc: 0.9716 - val_loss: 0.1904 - val_acc: 0.9233\n",
      "Epoch 11/20\n",
      "740/740 [==============================] - 238s 322ms/step - loss: 0.0609 - acc: 0.9892 - val_loss: 0.1755 - val_acc: 0.9267\n",
      "Epoch 12/20\n",
      "740/740 [==============================] - 238s 322ms/step - loss: 0.0385 - acc: 0.9959 - val_loss: 0.1701 - val_acc: 0.9300\n",
      "Epoch 13/20\n",
      "740/740 [==============================] - 239s 323ms/step - loss: 0.0308 - acc: 0.9986 - val_loss: 0.1629 - val_acc: 0.9333\n",
      "Epoch 14/20\n",
      "740/740 [==============================] - 239s 323ms/step - loss: 0.0273 - acc: 1.0000 - val_loss: 0.1687 - val_acc: 0.9467\n",
      "Epoch 15/20\n",
      "740/740 [==============================] - 240s 324ms/step - loss: 0.0307 - acc: 0.9986 - val_loss: 0.1563 - val_acc: 0.9433\n",
      "Epoch 16/20\n",
      "740/740 [==============================] - 239s 323ms/step - loss: 0.0231 - acc: 0.9986 - val_loss: 0.1714 - val_acc: 0.9467\n",
      "Epoch 17/20\n",
      "740/740 [==============================] - 239s 323ms/step - loss: 0.0146 - acc: 1.0000 - val_loss: 0.1639 - val_acc: 0.9400\n",
      "Epoch 20/20\n",
      "740/740 [==============================] - 240s 324ms/step - loss: 0.0093 - acc: 1.0000 - val_loss: 0.1566 - val_acc: 0.9300\n"
     ]
    }
   ],
   "source": [
    "vgg16_model.compile(optimizer='adam',\n",
    "              loss = 'binary_crossentropy',\n",
    "              metrics = ['accuracy'])\n",
    "\n",
    "BATCH_SIZE = 64\n",
    "EPOCHS = 20\n",
    "\n",
    "history = vgg16_model.fit(\n",
    "    X_train_norm, \n",
    "    y_train,  # prepared data\n",
    "    batch_size=BATCH_SIZE,\n",
    "    epochs=EPOCHS,\n",
    "    validation_data=(X_test_norm, y_test),\n",
    "    verbose=1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_train_val_accuracy(history.history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qgFuBdAB3vwP4IPAXZtYNtAHXursD3Wb2BeAxIBVY7e6vx6vOYeqnoiCiPQwRkVDcAsPd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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8e133a8cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_train_val_loss(history.history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
